{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Day23 进阶绘图类型——区域填充\n",
    "\n",
    "　　还记得之前**色彩篇**中我们绘制彩虹时使用到的`fill_between()`吗，当时我们只是简单介绍了其功能，而今天，我们来详细学习其常用知识。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-11T14:29:32.428763Z",
     "iopub.status.busy": "2020-11-11T14:29:32.428763Z",
     "iopub.status.idle": "2020-11-11T14:29:32.725966Z",
     "shell.execute_reply": "2020-11-11T14:29:32.725966Z",
     "shell.execute_reply.started": "2020-11-11T14:29:32.428763Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matplotlib版本： 3.1.3\n"
     ]
    }
   ],
   "source": [
    "import matplotlib;print('matplotlib版本：', matplotlib.__version__)\n",
    "import matplotlib.pyplot as plt # 从matplotlib中导入我们最经常使用的pyplot子模\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] # 设定默认字体为SimHei以解决中文显示乱码问题\n",
    "plt.rcParams['axes.unicode_minus'] = False # 解决图像负号'-'不正常显示的问题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "　　`fill_between()`最基本的参数为`x`、`y1`以及`y2`，其中`y2`是可选的，当未传入`y2`时自动将y=0的水平线作为`y2`。这些参数我们之前的日程介绍过，效果如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-11T14:29:32.726964Z",
     "iopub.status.busy": "2020-11-11T14:29:32.726964Z",
     "iopub.status.idle": "2020-11-11T14:29:32.845646Z",
     "shell.execute_reply": "2020-11-11T14:29:32.845646Z",
     "shell.execute_reply.started": "2020-11-11T14:29:32.726964Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "x = np.array([0, 1, 2, 3])\n",
    "y1 = np.array([0.8, 0.8, 0.2, 0.2])\n",
    "y2 = np.array([0, 0, 1, 1])\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "\n",
    "ax.plot(x, y1, 'o--')\n",
    "ax.plot(x, y2, 'o--')\n",
    "ax.fill_between(x, y1, y2, alpha=0.4);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "　　而`fill_between()`另一个常用参数是`where`，它传入与`x`、`y1`以及`y2`长度一致的bool值序列，用于控制对哪些成对y之间进行填充："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-11T14:29:32.846644Z",
     "iopub.status.busy": "2020-11-11T14:29:32.846644Z",
     "iopub.status.idle": "2020-11-11T14:29:32.949370Z",
     "shell.execute_reply": "2020-11-11T14:29:32.949370Z",
     "shell.execute_reply.started": "2020-11-11T14:29:32.846644Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "\n",
    "ax.plot(x, y1, 'o--')\n",
    "ax.plot(x, y2, 'o--')\n",
    "\n",
    "# 绘制y1大于y2之间的区域\n",
    "ax.fill_between(x, y1, y2, where=y1 > y2, alpha=0.4);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "　　但这种情况看起来有些不自然，因为并不是所有<font color=#a5c8e1>蓝色线条</font>与<font color=#ff983e>橙色线条</font>之间的区域都被填充绘制，而`fill_between()`中的参数`interpolate`就可以帮助我们解决这个问题："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-11T14:29:32.950368Z",
     "iopub.status.busy": "2020-11-11T14:29:32.950368Z",
     "iopub.status.idle": "2020-11-11T14:29:33.066057Z",
     "shell.execute_reply": "2020-11-11T14:29:33.066057Z",
     "shell.execute_reply.started": "2020-11-11T14:29:32.950368Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "\n",
    "ax.plot(x, y1, 'o--')\n",
    "ax.plot(x, y2, 'o--')\n",
    "\n",
    "# 设置interpolate=True\n",
    "ax.fill_between(x, y1, y2, where=y1 > y2, alpha=0.4, interpolate=True)\n",
    "ax.fill_between(x, y1, y2, where=y1 < y2, alpha=0.4, interpolate=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 课后测验：\n",
    "\n",
    "　　接下来的时间交给你们，在今天以及过往所学知识的基础上，请你揣测出下图可能的绘制方法，并基于下列的代码片段，尝试还原出下图：\n",
    "\n",
    "<center>\n",
    "    <img src=\"imgs/课后题目.png\" width=600></img>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-11T14:29:33.068051Z",
     "iopub.status.busy": "2020-11-11T14:29:33.067054Z",
     "iopub.status.idle": "2020-11-11T14:29:33.182768Z",
     "shell.execute_reply": "2020-11-11T14:29:33.182768Z",
     "shell.execute_reply.started": "2020-11-11T14:29:33.068051Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y1 = np.random.normal(0, 1, 10000)\n",
    "y2 = np.random.normal(2, 1, 10000)\n",
    "y3 = np.random.normal(3, 1, 10000)\n",
    "y4 = np.random.normal(4, 1, 10000)\n",
    "y5 = np.random.normal(5, 1, 10000)\n",
    "\n",
    "density1 = np.histogram(y1, density=True, bins=50)\n",
    "density2 = np.histogram(y2, density=True, bins=50)\n",
    "density3 = np.histogram(y3, density=True, bins=50)\n",
    "density4 = np.histogram(y4, density=True, bins=50)\n",
    "density5 = np.histogram(y5, density=True, bins=50)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "\n",
    "l=[density1,density2,density3,density4,density5]\n",
    "for i in range(0,5):\n",
    "    axin =  ax.inset_axes(bounds=[0.15*i, 0.2*i, 0.8, 0.3])\n",
    "    axin.fill_between(l[i][1][:-1], l[i][0], facecolor='#42737190')\n",
    "    axin.plot(l[i][1][:-1], l[i][0], color='#427371')\n",
    "    axin.axis('off')\n",
    "ax.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 请在今天对应的帖子下回复你的代码和结果"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.7 64-bit ('work': conda)",
   "language": "python",
   "name": "python37764bitworkconda950931f255cf497188ce70d743cc6efd"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
